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Single-cell annotation skills with omicverse

Predict cell-type labels for scRNA-seq and multimodal single-cell datasets.

1.2k|145|Updated Mar 22, 2021
Search Tags:#LLM#AI#bioinformatics#cell type#single-cell annotation#omicverse#label transfer#ontology mapping
One-click install
npx skills add https://github.com/Starlitnightly/omicverse --skill single-cell-annotation-skills-with-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Single-cell annotation skills with omicverse
Source: https://github.com/Starlitnightly/omicverse/tree/main/.claude/skills/single-annotation
Command: npx skills add https://github.com/Starlitnightly/omicverse --skill single-cell-annotation-skills-with-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires omicverse, scanpy, anndata, pandas, numpy, matplotlib, pertpy, scvi-tools, sentence-transformers, openai, torch, and includes references (resource) components.

What problem does it solves? Accurately annotating cell types in single-cell RNA-seq data is a critical but often manual and complex step. This Skill provides a comprehensive suite of automated and AI-powered annotation methods to streamline this process.

Core Features & Use Cases

  • Automated Cluster Annotation: Utilize SCSA, MetaTiME, and GPTAnno for rapid, AI-driven cell type assignments.
  • Consensus & Ontology Mapping: Achieve robust labels with CellVote consensus and align to ontologies using CellMatch.
  • Label Transfer: Propagate annotations across different single-cell modalities using weighted KNN.
  • Use Case: Annotate a complex single-cell dataset from a developing organ, combining multiple methods like SCSA for initial labels, CellMatch for ontology alignment, and CellVote for consensus, then transfer these labels to a related ATAC-seq dataset.

Quick Start

Run SCSA with CellMarker on my PBMC3k data, then use CellVote to get consensus labels, and finally map them to Cell Ontology terms.

Frequently Asked Questions about Single-cell annotation skills with omicverse

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate cell type annotation in single-cell RNA-seq data?▼

Automate cell type annotation by running SCSA, MetaTiME, or GPTAnno on normalized scRNA-seq data to predict labels per cluster or per cell, then append predictions to your AnnData object with supporting metadata and provenance for reproducibility.

Can I use multiple annotation methods together to get consensus labels?▼

Yes, CellVote combines predictions from SCSA, MetaTiME, CellMatch, and other methods to produce robust consensus cell-type labels, reducing annotation uncertainty across your single-cell dataset.

How do I map predicted cell types to a standardized ontology?▼

CellMatch aligns predicted cell-type labels to Cell Ontology terms, ensuring your annotations conform to community standards and enabling cross-study comparisons.

Can I transfer annotations from scRNA-seq to ATAC-seq or other modalities?▼

Weighted KNN label transfer propagates scRNA-seq cell-type annotations to multimodal datasets like ATAC-seq, leveraging shared biological relationships across different single-cell modalities.

What preprocessing steps are required before annotation?▼

Prepare input data by normalizing counts, reducing dimensionality, constructing neighbor graphs, and clustering; the Skill handles marker interpretation and integrates predictions into your AnnData object with full provenance.

Does this work with large-scale single-cell datasets?▼

Yes, the annotation pipeline supports scalable inference on complex tissues and developing organs, enabling per-cluster and per-cell workflows across large scRNA-seq and multimodal datasets with integrated cross-method results.

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